Treatment dose increase versus comedication in allergic rhinitis: Systematic review with dose-response network meta-analysis protocol
Bibliographic record
Abstract
Background: In patients with allergic rhinitis who remain uncontrolled despite being treated with standard daily doses of a single medication, strategies that can be followed to achieve adequate symptom control include (i) increasing the medication dose, or (ii) adding an additional medication (co-medication). However, it is not known which one of these two strategies is more effective. Objectives: This article describes a protocol of a systematic review with dose-response network meta-analysis aiming at comparing different intranasal and oral medications in patients with seasonal or perennial allergic rhinitis. Methods: We will search four electronic bibliographic databases and three clinical trials databases for randomized controlled trials (i) assessing adults or children with seasonal or perennial allergic rhinitis, and (ii) evaluating the effect of intranasal medications, oral medications or combinations of any intranasal and/or oral medications for allergic rhinitis. Assessed outcomes will include the Total Nasal Symptom Score, the Total Ocular Symptom Score and the Rhinoconjunctivitis Quality-of-Life Questionnaire (RQLQ). We will assess the methodological quality of included primary studies by using the Cochrane riskof-bias tool. Certainty in the body of evidence for the analysed outcomes will be assessed using the Grading of Recommendations Assessment, Development and Evaluation approach for network meta-analyses (GRADE-NMA). We will perform a frequentist dose-response model-based network meta-analysis. Sensitivity analyses will be performed by separately analysing data for seasonal and perennial allergic rhinitis. Conclusion: This protocol describes the methodology of a systematic review that will fit dose-response network meta-analysis models in the comparison between different intranasal and oral medications. The findings of this systematic review will support recommendations in the Allergic Rhinitis and its Impact on Asthma (ARIA) 2024-2025 guidelines.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.059 | 0.100 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.021 | 0.032 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.056 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".